The assumption that an AI assistant automatically frees up schedule is a shortcut that blinds managers to a hidden trade‑off. An AI can churn out text faster than a human, but the speed invites a cascade of revisions, context checks, and alignment meetings that consume the very time the tool was meant to save.

The model’s output often arrives with gaps—missing data, ambiguous phrasing, or style mismatches—that require a subject‑matter expert to step in, validate, and sometimes rewrite. In one real case, a consulting practice introduced a generative tool for proposal writing; the junior writers spent the morning feeding prompts, then spent the afternoon in back‑and‑forth calls with senior partners to reconcile the draft with the firm’s branding guidelines.

The net result was longer workdays and a backlog of internal reviews that delayed other billable work. The core dynamic is that speed begets “rework latency,” a feedback loop where faster output creates more downstream coordination, eroding the promised time savings.